Automate AMI Sorting for Your Affordable Housing Waitlist
Custom software can automatically sort affordable housing applicants by AMI percentage. Syntora engineers custom solutions that integrate with your existing RealPage or AppFolio environments.
Syntora designs and engineers custom software solutions to automatically sort affordable housing applicants by AMI percentage. Our approach involves building tailored integrations with existing property management systems and leveraging AI for robust income document processing. This enables efficient compliance and streamlines applicant management for housing providers.
The system's scope would depend on your portfolio's specific compliance rules. A property with a single LIHTC layer and standard W-2 applicants suggests a more straightforward build. A portfolio with layered HOME funds, student status restrictions, and frequent self-employment income would require more complex parsing and income anticipation logic.
We have experience building robust document processing pipelines using Claude API for sensitive financial documents, and the same architectural patterns apply to affordable housing income verification. An engagement typically involves an initial discovery phase to understand your specific regulatory requirements and existing workflows.
What Problem Does This Solve?
Property management systems like RealPage and AppFolio are great for lease management but their affordable housing modules fall short on automation. They require leasing teams to manually read paystubs, calculate anticipated 12-month income, and then select the correct AMI percentage from a dropdown menu. This workflow turns trained leasing agents into expensive data entry clerks and creates significant compliance risk.
A single mistake in these manual calculations can have serious consequences. Consider a leasing agent processing 30 applications for a 500-unit property. They misinterpret a bonus structure on a paystub, calculating an applicant's annual income at $42,000 instead of $45,000. This places the applicant in the 50% AMI tier instead of the correct 60% AMI tier. The error isn't caught for weeks, wasting everyone's time and potentially violating fair housing rules if the unit goes to someone further down the waitlist.
This manual process does not scale, especially during a lease-up where hundreds of applications arrive at once. The bottleneck isn't the software; it's the human-powered, error-prone process of turning income documents into a single, accurate number. Without automation, property managers are forced to either hire more staff or accept a slow, risky application review process.
How Would Syntora Approach This?
Syntora's approach would involve engineering a dedicated service tailored to your operational environment, integrating directly with RealPage or AppFolio. The initial phase would include a detailed audit of your current application submission process and compliance requirements to inform the architecture.
Upon an applicant submitting an application, a webhook would trigger an AWS Lambda function, passing the applicant data and attached income documents. This event-driven architecture is designed for near-instant processing, avoiding processing queues. The Lambda function would route income documents (PDFs, JPEGs of paystubs, W-2s) to the Claude API. Syntora would craft and refine prompts to precisely extract key data points such as hourly wage, hours per week, YTD earnings, overtime, tips, and bonuses.
A custom Python script would then execute your specific income anticipation logic, annualizing wages (e.g., hours x 2080) and projecting other income sources over the next 12 months, adhering to your jurisdictional rules. For similar document processing workloads, this parsing and calculation workflow typically completes within a couple of minutes.
Once the anticipated annual income is calculated, the system would query a Supabase database. This database would house the current year's AMI tables specific to your properties, which we would populate and maintain. The custom solution would match the applicant's income and household size to the correct AMI bucket (e.g., 30%, 50%, 60%). Finally, it would use the RealPage or AppFolio API to write this AMI percentage directly into a custom field on the applicant's record. Updates via these APIs are generally reflected in a Property Management System within a few seconds.
The delivered system would integrate directly into your existing PMS, enabling your leasing team to filter waitlists by the auto-populated AMI field. This allows efficient applicant sorting for specific AMI-restricted units. Depending on requirements, the solution could also include configuration for automated email confirmations to applicants, streamlining communication and reducing manual follow-up. Typical build timelines for this complexity range from 8 to 16 weeks, depending on the number of integration points and custom rule sets. Clients would need to provide detailed documentation of their compliance rules and access to their PMS APIs.
What Are the Key Benefits?
Go Live in 4 Weeks, Not 4 Months
From kickoff to a fully integrated production system in 20 business days. Your team can start processing applications automatically before the end of the month.
Eliminate 40+ Hours of Weekly Data Entry
Stop paying leasing agents to be calculators. The system handles the tedious income verification, freeing your team to focus on resident communication and closing leases.
You Own the Python Source Code
We deliver the entire codebase in a private GitHub repository. You are not locked into a SaaS platform and have full control to modify the system in the future.
Monitoring That Catches Errors for You
We set up AWS CloudWatch alarms that send a Slack message if an income document fails to parse after 3 attempts, turning a system-wide issue into a single alert.
Works Inside RealPage and AppFolio
This system pushes data directly into your existing PMS. There is no new dashboard to learn, no separate login to manage, and no change to your team's core workflow.
What Does the Process Look Like?
API Access and Rules Audit (Week 1)
You provide API credentials for your PMS. We map out your specific income calculation rules, AMI tables, and any unique requirements for layered funding like HOME or HUD.
Core System Build (Weeks 2-3)
We build the FastAPI service, Claude API integration for document parsing, and Supabase schema. You receive a detailed technical spec and a staging environment for testing.
PMS Integration and Go-Live (Week 4)
We connect the automation to your live PMS instance and activate the webhooks for new applications. You receive a runbook documenting the architecture and monitoring alerts.
Monitoring and Handoff (Weeks 5-8)
We actively monitor all production pipelines for four weeks, tuning prompts and handling edge cases. We then transition primary alert ownership to your team.
Frequently Asked Questions
- What's the typical cost and timeline for this system?
- The timeline is typically 4-6 weeks. Cost is a one-time build fee based on the number of unique property rules and income sources, not per unit or per user. We provide a fixed-price proposal after a discovery call where we review your specific operational needs. Book a discovery call at cal.com/syntora/discover.
- What happens if an income document is unreadable?
- If the Claude API cannot parse a document after two automated retries, the system flags the applicant in your PMS with a status like 'Manual Review Required' and sends an alert. This turns an entirely manual process into an exception-handling one for fewer than 5% of applicants, allowing your team to focus only on the complex cases.
- How is this different from using the built-in RealPage Affordable modules?
- RealPage's modules are for compliance tracking but still require manual data entry for income calculation. Their system does not automatically read pay stubs to anticipate future income. Syntora builds the automation layer that feeds accurate, pre-calculated data into RealPage, eliminating the manual bottleneck that slows down your team.
- How do you handle sensitive applicant data and PII?
- We do not store personally identifiable information (PII) long-term. Applicant data passes through the serverless functions for processing and is not persisted. The system is built on AWS and Supabase, inheriting their security postures. All data in transit is encrypted with TLS 1.2, and we provide a data processing agreement detailing all controls.
- Can the system handle self-employment or gig work income?
- Yes. We customize the document parsing logic for non-traditional income. For a freelancer, it can analyze a Profit & Loss statement to find net income. For a gig worker, it can parse earnings summaries. We identify all your common income sources during the audit phase and build specific prompts for each one.
- Does this work for a small portfolio or just large lease-ups?
- The serverless architecture on AWS Lambda means hosting costs are minimal, often under $50/month, regardless of scale. While the ROI is fastest on a large lease-up (500+ units), any property manager manually processing more than 20 applications a week will see significant time savings and a reduction in compliance risk.
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